如何修复CountVectorizer-在烧瓶中部署ml模型时词汇不正确

Question

from sklearn.feature_extraction.text import CountVectorizer
import flask
import pickle

with open('model/KTcategory-predictor.pkl', 'rb') as f:
    model = pickle.load(f)

with open('model/KTtfidf.pkl', 'rb') as f:
    tfidf = pickle.load(f)

app = flask.Flask(__name__, template_folder='templates')

@app.route('/', methods=['GET', 'POST'])

def main():
    count_vectorizer = CountVectorizer()

    if flask.request.method == 'GET':
        return(flask.render_template('main.html'))

    if flask.request.method == 'POST':
        text = flask.request.form['text'`
        text = text.split(" ")

        input_tc = count_vectorizer.transform(text)
        input_tfidf = tfidf.transform(input_tc)

        predictions = model.predict(input_tfidf)

        return (predictions)

if __name__ == '__main__':
    app.run(debug = True)